Overview

Dataset statistics

Number of variables25
Number of observations4703
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory955.3 KiB
Average record size in memory208.0 B

Variable types

Text7
Numeric16
Categorical2

Alerts

actor_1_fb_likes is highly overall correlated with actor_2_fb_likes and 2 other fieldsHigh correlation
actor_2_fb_likes is highly overall correlated with actor_1_fb_likes and 2 other fieldsHigh correlation
actor_3_fb_likes is highly overall correlated with actor_1_fb_likes and 2 other fieldsHigh correlation
budget is highly overall correlated with gross and 1 other fieldsHigh correlation
cast_total_fb_likes is highly overall correlated with actor_1_fb_likes and 2 other fieldsHigh correlation
gross is highly overall correlated with budget and 2 other fieldsHigh correlation
num_critic_for_reviews is highly overall correlated with num_user_for_reviews and 1 other fieldsHigh correlation
num_user_for_reviews is highly overall correlated with gross and 2 other fieldsHigh correlation
num_voted_users is highly overall correlated with budget and 3 other fieldsHigh correlation
content_rating is highly imbalanced (50.7%)Imbalance
budget is highly skewed (γ1 = 49.02395721)Skewed
director_fb_likes has 825 (17.5%) zerosZeros
actor_3_fb_likes has 66 (1.4%) zerosZeros
facenumber_in_poster has 2019 (42.9%) zerosZeros
movie_fb_likes has 2086 (44.4%) zerosZeros

Reproduction

Analysis started2024-04-10 14:49:06.627578
Analysis finished2024-04-10 14:49:54.487210
Duration47.86 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct2244
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-10T16:49:54.737290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length32
Median length24
Mean length13.085477
Min length3

Characters and Unicode

Total characters61541
Distinct characters76
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1378 ?
Unique (%)29.3%

Sample

1st rowJames Cameron
2nd rowGore Verbinski
3rd rowSam Mendes
4th rowChristopher Nolan
5th rowAndrew Stanton
ValueCountFrequency (%)
john 173
 
1.8%
david 143
 
1.5%
michael 123
 
1.3%
james 85
 
0.9%
peter 83
 
0.8%
robert 81
 
0.8%
richard 79
 
0.8%
paul 73
 
0.7%
scott 65
 
0.7%
steven 57
 
0.6%
Other values (2797) 8812
90.2%
2024-04-10T16:49:55.204228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5822
 
9.5%
5071
 
8.2%
a 4984
 
8.1%
n 4437
 
7.2%
r 4250
 
6.9%
o 3622
 
5.9%
i 3505
 
5.7%
l 2831
 
4.6%
t 2213
 
3.6%
s 1985
 
3.2%
Other values (66) 22821
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46161
75.0%
Uppercase Letter 9983
 
16.2%
Space Separator 5071
 
8.2%
Other Punctuation 243
 
0.4%
Dash Punctuation 83
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5822
12.6%
a 4984
10.8%
n 4437
9.6%
r 4250
 
9.2%
o 3622
 
7.8%
i 3505
 
7.6%
l 2831
 
6.1%
t 2213
 
4.8%
s 1985
 
4.3%
h 1761
 
3.8%
Other values (31) 10751
23.3%
Uppercase Letter
ValueCountFrequency (%)
S 971
 
9.7%
J 872
 
8.7%
M 843
 
8.4%
R 725
 
7.3%
C 674
 
6.8%
B 636
 
6.4%
D 580
 
5.8%
A 542
 
5.4%
L 474
 
4.7%
G 465
 
4.7%
Other values (21) 3201
32.1%
Other Punctuation
ValueCountFrequency (%)
. 224
92.2%
' 19
 
7.8%
Space Separator
ValueCountFrequency (%)
5071
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 83
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56144
91.2%
Common 5397
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5822
 
10.4%
a 4984
 
8.9%
n 4437
 
7.9%
r 4250
 
7.6%
o 3622
 
6.5%
i 3505
 
6.2%
l 2831
 
5.0%
t 2213
 
3.9%
s 1985
 
3.5%
h 1761
 
3.1%
Other values (62) 20734
36.9%
Common
ValueCountFrequency (%)
5071
94.0%
. 224
 
4.2%
- 83
 
1.5%
' 19
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61404
99.8%
None 137
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5822
 
9.5%
5071
 
8.3%
a 4984
 
8.1%
n 4437
 
7.2%
r 4250
 
6.9%
o 3622
 
5.9%
i 3505
 
5.7%
l 2831
 
4.6%
t 2213
 
3.6%
s 1985
 
3.2%
Other values (46) 22684
36.9%
None
ValueCountFrequency (%)
é 43
31.4%
á 18
13.1%
ó 16
 
11.7%
ö 15
 
10.9%
ñ 7
 
5.1%
í 7
 
5.1%
å 6
 
4.4%
ç 5
 
3.6%
É 3
 
2.2%
Ô 2
 
1.5%
Other values (10) 15
 
10.9%

num_critic_for_reviews
Real number (ℝ)

HIGH CORRELATION 

Distinct527
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.771
Minimum1
Maximum813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:49:55.414037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q157
median117
Q3200
95-th percentile391
Maximum813
Range812
Interquartile range (IQR)143

Descriptive statistics

Standard deviation120.99175
Coefficient of variation (CV)0.83001251
Kurtosis2.8708933
Mean145.771
Median Absolute Deviation (MAD)68
Skewness1.5050244
Sum685561
Variance14639.004
MonotonicityNot monotonic
2024-04-10T16:49:55.611722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 32
 
0.7%
43 30
 
0.6%
63 29
 
0.6%
25 29
 
0.6%
97 28
 
0.6%
50 28
 
0.6%
29 28
 
0.6%
16 28
 
0.6%
64 28
 
0.6%
112 28
 
0.6%
Other values (517) 4415
93.9%
ValueCountFrequency (%)
1 21
0.4%
2 17
0.4%
3 9
 
0.2%
4 14
0.3%
5 26
0.6%
6 15
0.3%
7 18
0.4%
8 24
0.5%
9 24
0.5%
10 26
0.6%
ValueCountFrequency (%)
813 1
< 0.1%
775 1
< 0.1%
765 1
< 0.1%
750 2
< 0.1%
739 1
< 0.1%
738 1
< 0.1%
733 1
< 0.1%
723 1
< 0.1%
712 1
< 0.1%
703 1
< 0.1%

duration
Real number (ℝ)

Distinct164
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.63066
Minimum14
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:49:55.816922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile84
Q194
median104
Q3118
95-th percentile146
Maximum330
Range316
Interquartile range (IQR)24

Descriptive statistics

Standard deviation22.562204
Coefficient of variation (CV)0.20769646
Kurtosis11.779179
Mean108.63066
Median Absolute Deviation (MAD)11
Skewness2.2280838
Sum510890
Variance509.05305
MonotonicityNot monotonic
2024-04-10T16:49:55.994863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 143
 
3.0%
100 134
 
2.8%
98 130
 
2.8%
101 130
 
2.8%
97 125
 
2.7%
93 120
 
2.6%
99 120
 
2.6%
94 120
 
2.6%
95 119
 
2.5%
106 108
 
2.3%
Other values (154) 3454
73.4%
ValueCountFrequency (%)
14 1
< 0.1%
20 1
< 0.1%
25 1
< 0.1%
34 1
< 0.1%
37 1
< 0.1%
41 1
< 0.1%
45 2
< 0.1%
46 1
< 0.1%
47 1
< 0.1%
53 1
< 0.1%
ValueCountFrequency (%)
330 1
< 0.1%
325 1
< 0.1%
300 1
< 0.1%
293 1
< 0.1%
289 1
< 0.1%
280 1
< 0.1%
271 1
< 0.1%
270 1
< 0.1%
251 2
< 0.1%
240 2
< 0.1%

director_fb_likes
Real number (ℝ)

ZEROS 

Distinct429
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean710.17223
Minimum0
Maximum23000
Zeros825
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:49:56.247430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median52
Q3209
95-th percentile1000
Maximum23000
Range23000
Interquartile range (IQR)201

Descriptive statistics

Standard deviation2861.8195
Coefficient of variation (CV)4.0297542
Kurtosis26.029513
Mean710.17223
Median Absolute Deviation (MAD)52
Skewness5.1181934
Sum3339940
Variance8190010.9
MonotonicityNot monotonic
2024-04-10T16:49:56.430225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 825
 
17.5%
3 65
 
1.4%
6 61
 
1.3%
7 58
 
1.2%
11 56
 
1.2%
2 56
 
1.2%
4 54
 
1.1%
10 51
 
1.1%
12 48
 
1.0%
5 48
 
1.0%
Other values (419) 3381
71.9%
ValueCountFrequency (%)
0 825
17.5%
2 56
 
1.2%
3 65
 
1.4%
4 54
 
1.1%
5 48
 
1.0%
6 61
 
1.3%
7 58
 
1.2%
8 47
 
1.0%
9 46
 
1.0%
10 51
 
1.1%
ValueCountFrequency (%)
23000 1
 
< 0.1%
22000 8
 
0.2%
21000 10
 
0.2%
18000 4
 
0.1%
17000 20
0.4%
16000 28
0.6%
15000 2
 
< 0.1%
14000 30
0.6%
13000 26
0.6%
12000 17
0.4%

actor_3_fb_likes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct905
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean662.18222
Minimum0
Maximum23000
Zeros66
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:49:56.600799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q1141
median383
Q3642
95-th percentile1000
Maximum23000
Range23000
Interquartile range (IQR)501

Descriptive statistics

Standard deviation1686.4062
Coefficient of variation (CV)2.5467404
Kurtosis57.760531
Mean662.18222
Median Absolute Deviation (MAD)251
Skewness7.1083848
Sum3114243
Variance2843966
MonotonicityNot monotonic
2024-04-10T16:49:56.778495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 123
 
2.6%
0 66
 
1.4%
11000 29
 
0.6%
2000 27
 
0.6%
3000 26
 
0.6%
3 22
 
0.5%
826 21
 
0.4%
7 21
 
0.4%
249 19
 
0.4%
322 18
 
0.4%
Other values (895) 4331
92.1%
ValueCountFrequency (%)
0 66
1.4%
2 16
 
0.3%
3 22
 
0.5%
4 18
 
0.4%
5 12
 
0.3%
6 17
 
0.4%
7 21
 
0.4%
8 15
 
0.3%
9 14
 
0.3%
10 12
 
0.3%
ValueCountFrequency (%)
23000 2
 
< 0.1%
20000 1
 
< 0.1%
19000 4
 
0.1%
17000 1
 
< 0.1%
16000 3
 
0.1%
15000 1
 
< 0.1%
14000 6
 
0.1%
13000 5
 
0.1%
12000 7
 
0.1%
11000 29
0.6%
Distinct2824
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-10T16:49:57.099702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length13.069105
Min length3

Characters and Unicode

Total characters61464
Distinct characters78
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1928 ?
Unique (%)41.0%

Sample

1st rowJoel David Moore
2nd rowOrlando Bloom
3rd rowRory Kinnear
4th rowChristian Bale
5th rowSamantha Morton
ValueCountFrequency (%)
michael 94
 
1.0%
david 53
 
0.5%
john 53
 
0.5%
james 50
 
0.5%
tom 48
 
0.5%
scott 48
 
0.5%
jason 42
 
0.4%
robert 41
 
0.4%
kevin 39
 
0.4%
adam 36
 
0.4%
Other values (3619) 9221
94.8%
2024-04-10T16:49:57.626352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5801
 
9.4%
a 5523
 
9.0%
5022
 
8.2%
n 4439
 
7.2%
r 4122
 
6.7%
i 3777
 
6.1%
o 3405
 
5.5%
l 3204
 
5.2%
t 2192
 
3.6%
s 2018
 
3.3%
Other values (68) 21961
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46208
75.2%
Uppercase Letter 9994
 
16.3%
Space Separator 5022
 
8.2%
Other Punctuation 173
 
0.3%
Dash Punctuation 61
 
0.1%
Decimal Number 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5801
12.6%
a 5523
12.0%
n 4439
9.6%
r 4122
8.9%
i 3777
 
8.2%
o 3405
 
7.4%
l 3204
 
6.9%
t 2192
 
4.7%
s 2018
 
4.4%
h 1697
 
3.7%
Other values (36) 10030
21.7%
Uppercase Letter
ValueCountFrequency (%)
M 938
 
9.4%
S 769
 
7.7%
C 760
 
7.6%
B 721
 
7.2%
J 712
 
7.1%
D 611
 
6.1%
A 584
 
5.8%
R 560
 
5.6%
L 475
 
4.8%
T 426
 
4.3%
Other values (16) 3438
34.4%
Other Punctuation
ValueCountFrequency (%)
. 114
65.9%
' 59
34.1%
Decimal Number
ValueCountFrequency (%)
0 3
50.0%
5 3
50.0%
Space Separator
ValueCountFrequency (%)
5022
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56202
91.4%
Common 5262
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5801
 
10.3%
a 5523
 
9.8%
n 4439
 
7.9%
r 4122
 
7.3%
i 3777
 
6.7%
o 3405
 
6.1%
l 3204
 
5.7%
t 2192
 
3.9%
s 2018
 
3.6%
h 1697
 
3.0%
Other values (62) 20024
35.6%
Common
ValueCountFrequency (%)
5022
95.4%
. 114
 
2.2%
- 61
 
1.2%
' 59
 
1.1%
0 3
 
0.1%
5 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61351
99.8%
None 113
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5801
 
9.5%
a 5523
 
9.0%
5022
 
8.2%
n 4439
 
7.2%
r 4122
 
6.7%
i 3777
 
6.2%
o 3405
 
5.6%
l 3204
 
5.2%
t 2192
 
3.6%
s 2018
 
3.3%
Other values (48) 21848
35.6%
None
ValueCountFrequency (%)
é 37
32.7%
í 14
 
12.4%
á 10
 
8.8%
ë 8
 
7.1%
ø 6
 
5.3%
ó 6
 
5.3%
å 4
 
3.5%
ü 4
 
3.5%
ç 3
 
2.7%
ï 3
 
2.7%
Other values (10) 18
15.9%

actor_1_fb_likes
Real number (ℝ)

HIGH CORRELATION 

Distinct843
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6817.3957
Minimum0
Maximum640000
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:49:57.819693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile116.1
Q1637
median1000
Q311000
95-th percentile24000
Maximum640000
Range640000
Interquartile range (IQR)10363

Descriptive statistics

Standard deviation14982.445
Coefficient of variation (CV)2.1976786
Kurtosis720.98565
Mean6817.3957
Median Absolute Deviation (MAD)790
Skewness19.549467
Sum32062212
Variance2.2447365 × 108
MonotonicityNot monotonic
2024-04-10T16:49:57.994838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 417
 
8.9%
11000 207
 
4.4%
2000 187
 
4.0%
3000 148
 
3.1%
12000 133
 
2.8%
13000 126
 
2.7%
14000 121
 
2.6%
10000 109
 
2.3%
18000 108
 
2.3%
22000 79
 
1.7%
Other values (833) 3068
65.2%
ValueCountFrequency (%)
0 14
0.3%
2 6
0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 4
 
0.1%
6 3
 
0.1%
7 2
 
< 0.1%
9 2
 
< 0.1%
11 2
 
< 0.1%
12 3
 
0.1%
ValueCountFrequency (%)
640000 1
 
< 0.1%
260000 2
 
< 0.1%
164000 2
 
< 0.1%
137000 2
 
< 0.1%
87000 8
 
0.2%
77000 1
 
< 0.1%
49000 27
0.6%
46000 1
 
< 0.1%
45000 5
 
0.1%
44000 2
 
< 0.1%

gross
Real number (ℝ)

HIGH CORRELATION 

Distinct4146
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45085643
Minimum162
Maximum7.6050585 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:49:58.177796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum162
5-th percentile100669.6
Q16494675
median24848292
Q354548936
95-th percentile1.7099911 × 108
Maximum7.6050585 × 108
Range7.6050568 × 108
Interquartile range (IQR)48054262

Descriptive statistics

Standard deviation64148103
Coefficient of variation (CV)1.4228055
Kurtosis16.705866
Mean45085643
Median Absolute Deviation (MAD)20807704
Skewness3.3289438
Sum2.1203778 × 1011
Variance4.1149791 × 1015
MonotonicityNot monotonic
2024-04-10T16:49:58.350578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24848292 458
 
9.7%
5000000 4
 
0.1%
3000000 3
 
0.1%
218051260 3
 
0.1%
177343675 3
 
0.1%
8000000 3
 
0.1%
13401683 2
 
< 0.1%
800000 2
 
< 0.1%
22494487 2
 
< 0.1%
21028755 2
 
< 0.1%
Other values (4136) 4221
89.8%
ValueCountFrequency (%)
162 1
< 0.1%
423 1
< 0.1%
607 1
< 0.1%
703 1
< 0.1%
721 1
< 0.1%
728 1
< 0.1%
828 1
< 0.1%
1029 1
< 0.1%
1100 1
< 0.1%
1111 1
< 0.1%
ValueCountFrequency (%)
760505847 1
< 0.1%
658672302 1
< 0.1%
652177271 1
< 0.1%
623279547 1
< 0.1%
533316061 1
< 0.1%
474544677 1
< 0.1%
460935665 1
< 0.1%
458991599 1
< 0.1%
448130642 1
< 0.1%
436471036 1
< 0.1%

genres
Text

Distinct878
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-10T16:49:58.568532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length64
Median length53
Mean length20.563045
Min length5

Characters and Unicode

Total characters96708
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique482 ?
Unique (%)10.2%

Sample

1st rowAction|Adventure|Fantasy|Sci-Fi
2nd rowAction|Adventure|Fantasy
3rd rowAction|Adventure|Thriller
4th rowAction|Thriller
5th rowAction|Adventure|Sci-Fi
ValueCountFrequency (%)
drama 209
 
4.4%
comedy 186
 
4.0%
comedy|drama|romance 182
 
3.9%
comedy|drama 180
 
3.8%
comedy|romance 150
 
3.2%
drama|romance 147
 
3.1%
crime|drama|thriller 94
 
2.0%
horror 64
 
1.4%
action|crime|thriller 63
 
1.3%
action|crime|drama|thriller 63
 
1.3%
Other values (868) 3365
71.6%
2024-04-10T16:49:58.993666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 9885
 
10.2%
| 8982
 
9.3%
a 8493
 
8.8%
e 7510
 
7.8%
m 6934
 
7.2%
i 6237
 
6.4%
o 5991
 
6.2%
y 4360
 
4.5%
n 4267
 
4.4%
t 3808
 
3.9%
Other values (23) 30241
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72855
75.3%
Uppercase Letter 14278
 
14.8%
Math Symbol 8982
 
9.3%
Dash Punctuation 593
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 9885
13.6%
a 8493
11.7%
e 7510
10.3%
m 6934
9.5%
i 6237
8.6%
o 5991
8.2%
y 4360
 
6.0%
n 4267
 
5.9%
t 3808
 
5.2%
l 3313
 
4.5%
Other values (9) 12057
16.5%
Uppercase Letter
ValueCountFrequency (%)
C 2617
18.3%
D 2505
17.5%
A 2216
15.5%
F 1687
11.8%
T 1331
9.3%
R 1060
7.4%
M 787
 
5.5%
S 768
 
5.4%
H 724
 
5.1%
W 291
 
2.0%
Other values (2) 292
 
2.0%
Math Symbol
ValueCountFrequency (%)
| 8982
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 87133
90.1%
Common 9575
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 9885
 
11.3%
a 8493
 
9.7%
e 7510
 
8.6%
m 6934
 
8.0%
i 6237
 
7.2%
o 5991
 
6.9%
y 4360
 
5.0%
n 4267
 
4.9%
t 3808
 
4.4%
l 3313
 
3.8%
Other values (21) 26335
30.2%
Common
ValueCountFrequency (%)
| 8982
93.8%
- 593
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 9885
 
10.2%
| 8982
 
9.3%
a 8493
 
8.8%
e 7510
 
7.8%
m 6934
 
7.2%
i 6237
 
6.4%
o 5991
 
6.2%
y 4360
 
4.5%
n 4267
 
4.4%
t 3808
 
3.9%
Other values (23) 30241
31.3%
Distinct1928
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-10T16:49:59.319625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length13.182011
Min length4

Characters and Unicode

Total characters61995
Distinct characters75
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1237 ?
Unique (%)26.3%

Sample

1st rowCCH Pounder
2nd rowJohnny Depp
3rd rowChristoph Waltz
4th rowTom Hardy
5th rowDaryl Sabara
ValueCountFrequency (%)
robert 106
 
1.1%
tom 90
 
0.9%
michael 83
 
0.9%
de 56
 
0.6%
jason 53
 
0.5%
steve 50
 
0.5%
james 50
 
0.5%
bruce 49
 
0.5%
jr 48
 
0.5%
niro 48
 
0.5%
Other values (2681) 9114
93.5%
2024-04-10T16:49:59.823806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5828
 
9.4%
a 5320
 
8.6%
5044
 
8.1%
n 4504
 
7.3%
r 4010
 
6.5%
i 3953
 
6.4%
o 3645
 
5.9%
l 3086
 
5.0%
t 2427
 
3.9%
s 2194
 
3.5%
Other values (65) 21984
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46671
75.3%
Uppercase Letter 10000
 
16.1%
Space Separator 5044
 
8.1%
Other Punctuation 210
 
0.3%
Dash Punctuation 68
 
0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5828
12.5%
a 5320
11.4%
n 4504
9.7%
r 4010
 
8.6%
i 3953
 
8.5%
o 3645
 
7.8%
l 3086
 
6.6%
t 2427
 
5.2%
s 2194
 
4.7%
h 1660
 
3.6%
Other values (31) 10044
21.5%
Uppercase Letter
ValueCountFrequency (%)
J 893
 
8.9%
M 851
 
8.5%
S 799
 
8.0%
C 762
 
7.6%
B 688
 
6.9%
D 680
 
6.8%
R 594
 
5.9%
H 495
 
5.0%
A 472
 
4.7%
L 463
 
4.6%
Other values (18) 3303
33.0%
Other Punctuation
ValueCountFrequency (%)
. 172
81.9%
' 38
 
18.1%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
0 1
50.0%
Space Separator
ValueCountFrequency (%)
5044
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56671
91.4%
Common 5324
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5828
 
10.3%
a 5320
 
9.4%
n 4504
 
7.9%
r 4010
 
7.1%
i 3953
 
7.0%
o 3645
 
6.4%
l 3086
 
5.4%
t 2427
 
4.3%
s 2194
 
3.9%
h 1660
 
2.9%
Other values (59) 20044
35.4%
Common
ValueCountFrequency (%)
5044
94.7%
. 172
 
3.2%
- 68
 
1.3%
' 38
 
0.7%
5 1
 
< 0.1%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61925
99.9%
None 70
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5828
 
9.4%
a 5320
 
8.6%
5044
 
8.1%
n 4504
 
7.3%
r 4010
 
6.5%
i 3953
 
6.4%
o 3645
 
5.9%
l 3086
 
5.0%
t 2427
 
3.9%
s 2194
 
3.5%
Other values (48) 21914
35.4%
None
ValueCountFrequency (%)
é 17
24.3%
ë 14
20.0%
á 6
 
8.6%
ç 5
 
7.1%
å 5
 
7.1%
í 4
 
5.7%
ø 4
 
5.7%
Á 2
 
2.9%
ñ 2
 
2.9%
ô 2
 
2.9%
Other values (7) 9
12.9%
Distinct4624
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-10T16:50:00.142603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length87
Median length59
Mean length16.306613
Min length2

Characters and Unicode

Total characters76690
Distinct characters92
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4547 ?
Unique (%)96.7%

Sample

1st rowAvatar 
2nd rowPirates of the Caribbean: At World's End 
3rd rowSpectre 
4th rowThe Dark Knight Rises 
5th rowJohn Carter 
ValueCountFrequency (%)
the 1499
 
11.5%
of 449
 
3.4%
a 173
 
1.3%
and 136
 
1.0%
in 115
 
0.9%
2 103
 
0.8%
to 98
 
0.8%
75
 
0.6%
man 64
 
0.5%
love 53
 
0.4%
Other values (4679) 10265
78.8%
2024-04-10T16:50:00.642372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8327
 
10.9%
e 7367
 
9.6%
  4703
 
6.1%
a 4512
 
5.9%
o 4351
 
5.7%
r 3863
 
5.0%
n 3846
 
5.0%
i 3689
 
4.8%
t 3562
 
4.6%
s 2816
 
3.7%
Other values (82) 29654
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50751
66.2%
Space Separator 13030
 
17.0%
Uppercase Letter 11422
 
14.9%
Other Punctuation 883
 
1.2%
Decimal Number 502
 
0.7%
Dash Punctuation 85
 
0.1%
Close Punctuation 4
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Currency Symbol 4
 
< 0.1%
Math Symbol 2
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7367
14.5%
a 4512
 
8.9%
o 4351
 
8.6%
r 3863
 
7.6%
n 3846
 
7.6%
i 3689
 
7.3%
t 3562
 
7.0%
s 2816
 
5.5%
h 2770
 
5.5%
l 2363
 
4.7%
Other values (22) 11612
22.9%
Uppercase Letter
ValueCountFrequency (%)
T 1612
14.1%
S 979
 
8.6%
M 774
 
6.8%
B 724
 
6.3%
D 677
 
5.9%
C 635
 
5.6%
A 624
 
5.5%
L 544
 
4.8%
H 524
 
4.6%
W 473
 
4.1%
Other values (17) 3856
33.8%
Other Punctuation
ValueCountFrequency (%)
: 349
39.5%
' 216
24.5%
. 135
 
15.3%
, 67
 
7.6%
& 59
 
6.7%
! 30
 
3.4%
? 15
 
1.7%
/ 7
 
0.8%
* 2
 
0.2%
# 2
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 145
28.9%
3 84
16.7%
0 78
15.5%
1 75
14.9%
4 35
 
7.0%
5 21
 
4.2%
8 19
 
3.8%
9 17
 
3.4%
6 14
 
2.8%
7 14
 
2.8%
Space Separator
ValueCountFrequency (%)
8327
63.9%
  4703
36.1%
Close Punctuation
ValueCountFrequency (%)
] 2
50.0%
) 2
50.0%
Open Punctuation
ValueCountFrequency (%)
[ 2
50.0%
( 2
50.0%
Currency Symbol
ValueCountFrequency (%)
$ 2
50.0%
¢ 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 85
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62173
81.1%
Common 14517
 
18.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7367
 
11.8%
a 4512
 
7.3%
o 4351
 
7.0%
r 3863
 
6.2%
n 3846
 
6.2%
i 3689
 
5.9%
t 3562
 
5.7%
s 2816
 
4.5%
h 2770
 
4.5%
l 2363
 
3.8%
Other values (49) 23034
37.0%
Common
ValueCountFrequency (%)
8327
57.4%
  4703
32.4%
: 349
 
2.4%
' 216
 
1.5%
2 145
 
1.0%
. 135
 
0.9%
- 85
 
0.6%
3 84
 
0.6%
0 78
 
0.5%
1 75
 
0.5%
Other values (23) 320
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71968
93.8%
None 4722
 
6.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8327
 
11.6%
e 7367
 
10.2%
a 4512
 
6.3%
o 4351
 
6.0%
r 3863
 
5.4%
n 3846
 
5.3%
i 3689
 
5.1%
t 3562
 
4.9%
s 2816
 
3.9%
h 2770
 
3.8%
Other values (71) 26865
37.3%
None
ValueCountFrequency (%)
  4703
99.6%
é 8
 
0.2%
½ 2
 
< 0.1%
¢ 2
 
< 0.1%
ñ 1
 
< 0.1%
á 1
 
< 0.1%
Æ 1
 
< 0.1%
ü 1
 
< 0.1%
· 1
 
< 0.1%
à 1
 
< 0.1%

num_voted_users
Real number (ℝ)

HIGH CORRELATION 

Distinct4593
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87783.318
Minimum5
Maximum1689764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:50:00.852834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile1099.4
Q110774
median37952
Q3101938
95-th percentile343205.1
Maximum1689764
Range1689759
Interquartile range (IQR)91164

Descriptive statistics

Standard deviation140733.28
Coefficient of variation (CV)1.6031894
Kurtosis23.651174
Mean87783.318
Median Absolute Deviation (MAD)32809
Skewness3.9557772
Sum4.1284494 × 108
Variance1.9805856 × 1010
MonotonicityNot monotonic
2024-04-10T16:50:01.027403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3119 3
 
0.1%
2541 3
 
0.1%
3665 3
 
0.1%
9903 2
 
< 0.1%
6069 2
 
< 0.1%
80639 2
 
< 0.1%
25870 2
 
< 0.1%
1231 2
 
< 0.1%
3943 2
 
< 0.1%
53 2
 
< 0.1%
Other values (4583) 4680
99.5%
ValueCountFrequency (%)
5 2
< 0.1%
19 1
< 0.1%
28 1
< 0.1%
37 1
< 0.1%
40 1
< 0.1%
47 1
< 0.1%
48 1
< 0.1%
50 1
< 0.1%
53 2
< 0.1%
59 1
< 0.1%
ValueCountFrequency (%)
1689764 1
< 0.1%
1676169 1
< 0.1%
1468200 1
< 0.1%
1347461 1
< 0.1%
1324680 1
< 0.1%
1251222 1
< 0.1%
1238746 1
< 0.1%
1217752 1
< 0.1%
1215718 1
< 0.1%
1155770 1
< 0.1%

cast_total_fb_likes
Real number (ℝ)

HIGH CORRELATION 

Distinct3841
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10073.469
Minimum0
Maximum656730
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:50:01.215380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile243
Q11500.5
median3227
Q314462.5
95-th percentile37605.9
Maximum656730
Range656730
Interquartile range (IQR)12962

Descriptive statistics

Standard deviation18234.161
Coefficient of variation (CV)1.8101174
Kurtosis372.68719
Mean10073.469
Median Absolute Deviation (MAD)2418
Skewness12.956554
Sum47375524
Variance3.3248463 × 108
MonotonicityNot monotonic
2024-04-10T16:50:01.418302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
0.3%
2020 6
 
0.1%
29 5
 
0.1%
673 5
 
0.1%
1044 5
 
0.1%
2321 4
 
0.1%
1936 4
 
0.1%
1761 4
 
0.1%
2486 4
 
0.1%
1136 4
 
0.1%
Other values (3831) 4648
98.8%
ValueCountFrequency (%)
0 14
0.3%
2 4
 
0.1%
4 2
 
< 0.1%
5 3
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
11 1
 
< 0.1%
13 1
 
< 0.1%
15 2
 
< 0.1%
ValueCountFrequency (%)
656730 1
< 0.1%
303717 1
< 0.1%
283939 1
< 0.1%
263584 1
< 0.1%
170118 1
< 0.1%
140268 1
< 0.1%
137712 1
< 0.1%
120797 1
< 0.1%
108016 1
< 0.1%
106759 1
< 0.1%
Distinct3312
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-10T16:50:01.738772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length13.072932
Min length3

Characters and Unicode

Total characters61482
Distinct characters81
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2494 ?
Unique (%)53.0%

Sample

1st rowWes Studi
2nd rowJack Davenport
3rd rowStephanie Sigman
4th rowJoseph Gordon-Levitt
5th rowPolly Walker
ValueCountFrequency (%)
michael 79
 
0.8%
john 71
 
0.7%
david 68
 
0.7%
james 64
 
0.7%
robert 46
 
0.5%
kevin 39
 
0.4%
paul 38
 
0.4%
tom 38
 
0.4%
peter 37
 
0.4%
steve 36
 
0.4%
Other values (4097) 9226
94.7%
2024-04-10T16:50:02.226661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5818
 
9.5%
a 5598
 
9.1%
5039
 
8.2%
n 4302
 
7.0%
r 3921
 
6.4%
i 3719
 
6.0%
o 3333
 
5.4%
l 3291
 
5.4%
t 2209
 
3.6%
s 2181
 
3.5%
Other values (71) 22071
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46129
75.0%
Uppercase Letter 10018
 
16.3%
Space Separator 5039
 
8.2%
Other Punctuation 219
 
0.4%
Dash Punctuation 75
 
0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5818
12.6%
a 5598
12.1%
n 4302
9.3%
r 3921
 
8.5%
i 3719
 
8.1%
o 3333
 
7.2%
l 3291
 
7.1%
t 2209
 
4.8%
s 2181
 
4.7%
h 1727
 
3.7%
Other values (34) 10030
21.7%
Uppercase Letter
ValueCountFrequency (%)
M 933
 
9.3%
J 781
 
7.8%
S 780
 
7.8%
B 751
 
7.5%
C 735
 
7.3%
D 611
 
6.1%
R 588
 
5.9%
A 545
 
5.4%
L 493
 
4.9%
K 436
 
4.4%
Other values (21) 3365
33.6%
Other Punctuation
ValueCountFrequency (%)
. 164
74.9%
' 55
 
25.1%
Decimal Number
ValueCountFrequency (%)
0 1
50.0%
5 1
50.0%
Space Separator
ValueCountFrequency (%)
5039
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56147
91.3%
Common 5335
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5818
 
10.4%
a 5598
 
10.0%
n 4302
 
7.7%
r 3921
 
7.0%
i 3719
 
6.6%
o 3333
 
5.9%
l 3291
 
5.9%
t 2209
 
3.9%
s 2181
 
3.9%
h 1727
 
3.1%
Other values (65) 20048
35.7%
Common
ValueCountFrequency (%)
5039
94.5%
. 164
 
3.1%
- 75
 
1.4%
' 55
 
1.0%
0 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61358
99.8%
None 124
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5818
 
9.5%
a 5598
 
9.1%
5039
 
8.2%
n 4302
 
7.0%
r 3921
 
6.4%
i 3719
 
6.1%
o 3333
 
5.4%
l 3291
 
5.4%
t 2209
 
3.6%
s 2181
 
3.6%
Other values (48) 21947
35.8%
None
ValueCountFrequency (%)
é 45
36.3%
á 13
 
10.5%
í 10
 
8.1%
ó 9
 
7.3%
ë 7
 
5.6%
ü 6
 
4.8%
à 5
 
4.0%
è 4
 
3.2%
ô 3
 
2.4%
å 3
 
2.4%
Other values (13) 19
15.3%

facenumber_in_poster
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3567935
Minimum0
Maximum43
Zeros2019
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:50:02.400803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum43
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0086637
Coefficient of variation (CV)1.4804491
Kurtosis55.770377
Mean1.3567935
Median Absolute Deviation (MAD)1
Skewness4.5646736
Sum6381
Variance4.03473
MonotonicityNot monotonic
2024-04-10T16:50:02.518051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 2019
42.9%
1 1179
25.1%
2 665
 
14.1%
3 359
 
7.6%
4 190
 
4.0%
5 100
 
2.1%
6 67
 
1.4%
7 45
 
1.0%
8 34
 
0.7%
9 15
 
0.3%
Other values (9) 30
 
0.6%
ValueCountFrequency (%)
0 2019
42.9%
1 1179
25.1%
2 665
 
14.1%
3 359
 
7.6%
4 190
 
4.0%
5 100
 
2.1%
6 67
 
1.4%
7 45
 
1.0%
8 34
 
0.7%
9 15
 
0.3%
ValueCountFrequency (%)
43 1
 
< 0.1%
31 1
 
< 0.1%
19 1
 
< 0.1%
15 5
 
0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 4
 
0.1%
11 5
 
0.1%
10 10
0.2%
9 15
0.3%
Distinct4620
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-10T16:50:02.834046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length149
Median length102
Mean length52.40825
Min length2

Characters and Unicode

Total characters246476
Distinct characters42
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4541 ?
Unique (%)96.6%

Sample

1st rowavatar|future|marine|native|paraplegic
2nd rowgoddess|marriage ceremony|marriage proposal|pirate|singapore
3rd rowbomb|espionage|sequel|spy|terrorist
4th rowdeception|imprisonment|lawlessness|police officer|terrorist plot
5th rowalien|american civil war|male nipple|mars|princess
ValueCountFrequency (%)
in 314
 
1.8%
of 212
 
1.2%
on 196
 
1.1%
the 185
 
1.1%
a 180
 
1.0%
to 174
 
1.0%
york 120
 
0.7%
female 102
 
0.6%
by 99
 
0.6%
based 98
 
0.6%
Other values (11164) 15596
90.3%
2024-04-10T16:50:03.389809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 23831
 
9.7%
a 18830
 
7.6%
| 18520
 
7.5%
i 17990
 
7.3%
r 17414
 
7.1%
t 15540
 
6.3%
n 15097
 
6.1%
o 14867
 
6.0%
s 12776
 
5.2%
12573
 
5.1%
Other values (32) 79038
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 214082
86.9%
Math Symbol 18520
 
7.5%
Space Separator 12573
 
5.1%
Decimal Number 1087
 
0.4%
Other Punctuation 212
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 23831
11.1%
a 18830
 
8.8%
i 17990
 
8.4%
r 17414
 
8.1%
t 15540
 
7.3%
n 15097
 
7.1%
o 14867
 
6.9%
s 12776
 
6.0%
l 10710
 
5.0%
c 9105
 
4.3%
Other values (16) 57922
27.1%
Decimal Number
ValueCountFrequency (%)
1 277
25.5%
0 258
23.7%
9 215
19.8%
2 74
 
6.8%
8 64
 
5.9%
7 47
 
4.3%
5 46
 
4.2%
3 41
 
3.8%
6 37
 
3.4%
4 28
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 126
59.4%
' 86
40.6%
Math Symbol
ValueCountFrequency (%)
| 18520
100.0%
Space Separator
ValueCountFrequency (%)
12573
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 214082
86.9%
Common 32394
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 23831
11.1%
a 18830
 
8.8%
i 17990
 
8.4%
r 17414
 
8.1%
t 15540
 
7.3%
n 15097
 
7.1%
o 14867
 
6.9%
s 12776
 
6.0%
l 10710
 
5.0%
c 9105
 
4.3%
Other values (16) 57922
27.1%
Common
ValueCountFrequency (%)
| 18520
57.2%
12573
38.8%
1 277
 
0.9%
0 258
 
0.8%
9 215
 
0.7%
. 126
 
0.4%
' 86
 
0.3%
2 74
 
0.2%
8 64
 
0.2%
7 47
 
0.1%
Other values (6) 154
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 246476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 23831
 
9.7%
a 18830
 
7.6%
| 18520
 
7.5%
i 17990
 
7.3%
r 17414
 
7.1%
t 15540
 
6.3%
n 15097
 
6.1%
o 14867
 
6.0%
s 12776
 
5.2%
12573
 
5.1%
Other values (32) 79038
32.1%

num_user_for_reviews
Real number (ℝ)

HIGH CORRELATION 

Distinct953
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284.75973
Minimum1
Maximum5060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:50:03.591219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q175
median166
Q3340
95-th percentile921.7
Maximum5060
Range5059
Interquartile range (IQR)265

Descriptive statistics

Standard deviation383.73636
Coefficient of variation (CV)1.3475795
Kurtosis25.963836
Mean284.75973
Median Absolute Deviation (MAD)113
Skewness4.0957567
Sum1339225
Variance147253.59
MonotonicityNot monotonic
2024-04-10T16:50:03.760321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 30
 
0.6%
50 25
 
0.5%
32 24
 
0.5%
53 22
 
0.5%
21 22
 
0.5%
31 22
 
0.5%
14 22
 
0.5%
39 21
 
0.4%
10 21
 
0.4%
27 21
 
0.4%
Other values (943) 4473
95.1%
ValueCountFrequency (%)
1 18
0.4%
2 10
0.2%
3 17
0.4%
4 11
0.2%
5 14
0.3%
6 17
0.4%
7 12
0.3%
8 14
0.3%
9 17
0.4%
10 21
0.4%
ValueCountFrequency (%)
5060 1
< 0.1%
4667 1
< 0.1%
4144 1
< 0.1%
3646 1
< 0.1%
3597 1
< 0.1%
3516 1
< 0.1%
3400 1
< 0.1%
3286 1
< 0.1%
3189 1
< 0.1%
3054 1
< 0.1%

country
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
USA
3575 
Other
708 
UK
420 

Length

Max length5
Median length3
Mean length3.2117797
Min length2

Characters and Unicode

Total characters15105
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSA
2nd rowUSA
3rd rowUK
4th rowUSA
5th rowUSA

Common Values

ValueCountFrequency (%)
USA 3575
76.0%
Other 708
 
15.1%
UK 420
 
8.9%

Length

2024-04-10T16:50:04.271034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T16:50:04.432301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
usa 3575
76.0%
other 708
 
15.1%
uk 420
 
8.9%

Most occurring characters

ValueCountFrequency (%)
U 3995
26.4%
S 3575
23.7%
A 3575
23.7%
O 708
 
4.7%
t 708
 
4.7%
h 708
 
4.7%
e 708
 
4.7%
r 708
 
4.7%
K 420
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12273
81.3%
Lowercase Letter 2832
 
18.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 3995
32.6%
S 3575
29.1%
A 3575
29.1%
O 708
 
5.8%
K 420
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
t 708
25.0%
h 708
25.0%
e 708
25.0%
r 708
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15105
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 3995
26.4%
S 3575
23.7%
A 3575
23.7%
O 708
 
4.7%
t 708
 
4.7%
h 708
 
4.7%
e 708
 
4.7%
r 708
 
4.7%
K 420
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 3995
26.4%
S 3575
23.7%
A 3575
23.7%
O 708
 
4.7%
t 708
 
4.7%
h 708
 
4.7%
e 708
 
4.7%
r 708
 
4.7%
K 420
 
2.8%

content_rating
Categorical

IMBALANCE 

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
R
2234 
PG-13
1419 
PG
680 
G
 
109
Not Rated
 
102
Other values (10)
 
159

Length

Max length9
Median length1
Mean length2.7016798
Min length1

Characters and Unicode

Total characters12706
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPG-13
2nd rowPG-13
3rd rowPG-13
4th rowPG-13
5th rowPG-13

Common Values

ValueCountFrequency (%)
R 2234
47.5%
PG-13 1419
30.2%
PG 680
 
14.5%
G 109
 
2.3%
Not Rated 102
 
2.2%
Unrated 57
 
1.2%
Approved 55
 
1.2%
X 13
 
0.3%
Passed 9
 
0.2%
NC-17 7
 
0.1%
Other values (5) 18
 
0.4%

Length

2024-04-10T16:50:04.562294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r 2234
46.5%
pg-13 1419
29.5%
pg 680
 
14.2%
g 109
 
2.3%
not 102
 
2.1%
rated 102
 
2.1%
unrated 57
 
1.2%
approved 55
 
1.1%
x 13
 
0.3%
passed 9
 
0.2%
Other values (6) 25
 
0.5%

Most occurring characters

ValueCountFrequency (%)
R 2336
18.4%
G 2218
17.5%
P 2115
16.6%
- 1433
11.3%
1 1429
11.2%
3 1419
11.2%
t 261
 
2.1%
e 223
 
1.8%
d 223
 
1.8%
a 168
 
1.3%
Other values (17) 881
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6929
54.5%
Decimal Number 2858
22.5%
Dash Punctuation 1433
 
11.3%
Lowercase Letter 1384
 
10.9%
Space Separator 102
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 2336
33.7%
G 2218
32.0%
P 2115
30.5%
N 109
 
1.6%
U 57
 
0.8%
A 55
 
0.8%
X 13
 
0.2%
C 7
 
0.1%
T 7
 
0.1%
V 7
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
t 261
18.9%
e 223
16.1%
d 223
16.1%
a 168
12.1%
o 157
11.3%
r 112
8.1%
p 110
7.9%
n 57
 
4.1%
v 55
 
4.0%
s 18
 
1.3%
Decimal Number
ValueCountFrequency (%)
1 1429
50.0%
3 1419
49.7%
7 7
 
0.2%
4 3
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1433
100.0%
Space Separator
ValueCountFrequency (%)
102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8313
65.4%
Common 4393
34.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 2336
28.1%
G 2218
26.7%
P 2115
25.4%
t 261
 
3.1%
e 223
 
2.7%
d 223
 
2.7%
a 168
 
2.0%
o 157
 
1.9%
r 112
 
1.3%
p 110
 
1.3%
Other values (11) 390
 
4.7%
Common
ValueCountFrequency (%)
- 1433
32.6%
1 1429
32.5%
3 1419
32.3%
102
 
2.3%
7 7
 
0.2%
4 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 2336
18.4%
G 2218
17.5%
P 2115
16.6%
- 1433
11.3%
1 1429
11.2%
3 1419
11.2%
t 261
 
2.1%
e 223
 
1.8%
d 223
 
1.8%
a 168
 
1.3%
Other values (17) 881
 
6.9%

budget
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct432
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39306827
Minimum218
Maximum1.22155 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:50:04.725675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum218
5-th percentile800000
Q17500000
median20000000
Q340000000
95-th percentile1.25 × 108
Maximum1.22155 × 1010
Range1.22155 × 1010
Interquartile range (IQR)32500000

Descriptive statistics

Standard deviation2.02669 × 108
Coefficient of variation (CV)5.1560762
Kurtosis2820.5211
Mean39306827
Median Absolute Deviation (MAD)15000000
Skewness49.023957
Sum1.8486001 × 1011
Variance4.1074723 × 1016
MonotonicityNot monotonic
2024-04-10T16:50:04.933609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000000 442
 
9.4%
30000000 145
 
3.1%
15000000 141
 
3.0%
25000000 139
 
3.0%
10000000 137
 
2.9%
40000000 131
 
2.8%
35000000 120
 
2.6%
50000000 104
 
2.2%
5000000 102
 
2.2%
60000000 94
 
2.0%
Other values (422) 3148
66.9%
ValueCountFrequency (%)
218 1
 
< 0.1%
1100 1
 
< 0.1%
4500 1
 
< 0.1%
7000 3
0.1%
9000 1
 
< 0.1%
10000 2
< 0.1%
14000 1
 
< 0.1%
15000 2
< 0.1%
20000 3
0.1%
22000 1
 
< 0.1%
ValueCountFrequency (%)
1.22155 × 10101
< 0.1%
4200000000 1
< 0.1%
2500000000 1
< 0.1%
2400000000 1
< 0.1%
2127519898 1
< 0.1%
1100000000 1
< 0.1%
1000000000 1
< 0.1%
700000000 2
< 0.1%
600000000 1
< 0.1%
553632000 1
< 0.1%

title_year
Real number (ℝ)

Distinct91
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.1112
Minimum1916
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:50:05.112895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1916
5-th percentile1978
Q11999
median2005
Q32010
95-th percentile2015
Maximum2016
Range100
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.50241
Coefficient of variation (CV)0.0062446132
Kurtosis7.3909201
Mean2002.1112
Median Absolute Deviation (MAD)6
Skewness-2.2877603
Sum9415929
Variance156.31026
MonotonicityNot monotonic
2024-04-10T16:50:05.291237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2009 252
 
5.4%
2006 235
 
5.0%
2008 222
 
4.7%
2010 221
 
4.7%
2011 215
 
4.6%
2005 215
 
4.6%
2014 214
 
4.6%
2013 213
 
4.5%
2004 206
 
4.4%
2012 203
 
4.3%
Other values (81) 2507
53.3%
ValueCountFrequency (%)
1916 1
< 0.1%
1920 1
< 0.1%
1925 1
< 0.1%
1927 1
< 0.1%
1929 2
< 0.1%
1930 1
< 0.1%
1932 1
< 0.1%
1933 2
< 0.1%
1934 1
< 0.1%
1935 1
< 0.1%
ValueCountFrequency (%)
2016 82
 
1.7%
2015 183
3.9%
2014 214
4.6%
2013 213
4.5%
2012 203
4.3%
2011 215
4.6%
2010 221
4.7%
2009 252
5.4%
2008 222
4.7%
2007 197
4.2%

actor_2_fb_likes
Real number (ℝ)

HIGH CORRELATION 

Distinct906
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1718.748
Minimum0
Maximum137000
Zeros32
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:50:05.465346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.1
Q1298.5
median617
Q3931
95-th percentile11000
Maximum137000
Range137000
Interquartile range (IQR)632.5

Descriptive statistics

Standard deviation4136.4756
Coefficient of variation (CV)2.4066794
Kurtosis249.6205
Mean1718.748
Median Absolute Deviation (MAD)316
Skewness9.7679393
Sum8083272
Variance17110430
MonotonicityNot monotonic
2024-04-10T16:50:05.637571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 296
 
6.3%
11000 110
 
2.3%
2000 98
 
2.1%
3000 74
 
1.6%
10000 46
 
1.0%
14000 40
 
0.9%
13000 40
 
0.9%
826 37
 
0.8%
4000 33
 
0.7%
0 32
 
0.7%
Other values (896) 3897
82.9%
ValueCountFrequency (%)
0 32
0.7%
2 11
 
0.2%
3 9
 
0.2%
4 10
 
0.2%
5 8
 
0.2%
6 7
 
0.1%
7 2
 
< 0.1%
8 9
 
0.2%
9 12
 
0.3%
10 8
 
0.2%
ValueCountFrequency (%)
137000 1
 
< 0.1%
29000 1
 
< 0.1%
27000 2
 
< 0.1%
25000 3
 
0.1%
23000 6
0.1%
22000 11
0.2%
21000 3
 
0.1%
20000 6
0.1%
19000 7
0.1%
18000 9
0.2%

imdb_score
Real number (ℝ)

Distinct76
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4313842
Minimum1.6
Maximum9.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:50:05.826390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.4
Q15.8
median6.6
Q37.2
95-th percentile8
Maximum9.3
Range7.7
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.098782
Coefficient of variation (CV)0.17084689
Kurtosis1.0533928
Mean6.4313842
Median Absolute Deviation (MAD)0.7
Skewness-0.7682876
Sum30246.8
Variance1.2073219
MonotonicityNot monotonic
2024-04-10T16:50:05.997769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7 212
 
4.5%
6.6 191
 
4.1%
6.5 181
 
3.8%
7.2 181
 
3.8%
6.4 180
 
3.8%
6.8 177
 
3.8%
7.3 175
 
3.7%
7.1 173
 
3.7%
7 173
 
3.7%
6.1 173
 
3.7%
Other values (66) 2887
61.4%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
1.7 1
 
< 0.1%
1.9 3
0.1%
2 2
< 0.1%
2.1 3
0.1%
2.2 3
0.1%
2.3 3
0.1%
2.4 2
< 0.1%
2.5 2
< 0.1%
2.6 1
 
< 0.1%
ValueCountFrequency (%)
9.3 1
 
< 0.1%
9.2 1
 
< 0.1%
9 2
 
< 0.1%
8.9 5
 
0.1%
8.8 5
 
0.1%
8.7 8
 
0.2%
8.6 11
 
0.2%
8.5 21
0.4%
8.4 23
0.5%
8.3 34
0.7%

aspect_ratio
Real number (ℝ)

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1255305
Minimum1.18
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:50:06.164777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.18
5-th percentile1.78
Q11.85
median2.35
Q32.35
95-th percentile2.35
Maximum16
Range14.82
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.63838629
Coefficient of variation (CV)0.3003421
Kurtosis377.18399
Mean2.1255305
Median Absolute Deviation (MAD)0
Skewness17.406589
Sum9996.37
Variance0.40753706
MonotonicityNot monotonic
2024-04-10T16:50:06.284322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2.35 2499
53.1%
1.85 1870
39.8%
1.37 97
 
2.1%
1.78 79
 
1.7%
1.66 63
 
1.3%
1.33 37
 
0.8%
2.2 14
 
0.3%
2.39 14
 
0.3%
16 8
 
0.2%
2 4
 
0.1%
Other values (10) 18
 
0.4%
ValueCountFrequency (%)
1.18 1
 
< 0.1%
1.2 1
 
< 0.1%
1.33 37
 
0.8%
1.37 97
2.1%
1.44 1
 
< 0.1%
1.5 2
 
< 0.1%
1.66 63
1.3%
1.75 3
 
0.1%
1.77 1
 
< 0.1%
1.78 79
1.7%
ValueCountFrequency (%)
16 8
 
0.2%
2.76 3
 
0.1%
2.55 2
 
< 0.1%
2.4 3
 
0.1%
2.39 14
 
0.3%
2.35 2499
53.1%
2.24 1
 
< 0.1%
2.2 14
 
0.3%
2 4
 
0.1%
1.85 1870
39.8%

movie_fb_likes
Real number (ℝ)

ZEROS 

Distinct836
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7779.7997
Minimum0
Maximum349000
Zeros2086
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-10T16:50:06.460359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median181
Q35000
95-th percentile41900
Maximum349000
Range349000
Interquartile range (IQR)5000

Descriptive statistics

Standard deviation19611.482
Coefficient of variation (CV)2.520821
Kurtosis40.309513
Mean7779.7997
Median Absolute Deviation (MAD)181
Skewness4.9742692
Sum36588398
Variance3.8461023 × 108
MonotonicityNot monotonic
2024-04-10T16:50:06.642311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2086
44.4%
1000 103
 
2.2%
11000 80
 
1.7%
10000 79
 
1.7%
12000 59
 
1.3%
13000 58
 
1.2%
2000 54
 
1.1%
15000 51
 
1.1%
14000 46
 
1.0%
16000 46
 
1.0%
Other values (826) 2041
43.4%
ValueCountFrequency (%)
0 2086
44.4%
4 2
 
< 0.1%
5 1
 
< 0.1%
7 2
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 2
 
< 0.1%
14 1
 
< 0.1%
16 1
 
< 0.1%
17 3
 
0.1%
ValueCountFrequency (%)
349000 1
< 0.1%
199000 1
< 0.1%
197000 1
< 0.1%
191000 1
< 0.1%
190000 1
< 0.1%
175000 1
< 0.1%
165000 1
< 0.1%
164000 1
< 0.1%
153000 1
< 0.1%
150000 1
< 0.1%

Interactions

2024-04-10T16:49:50.807431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:08.979564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:12.030247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:14.714853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:17.154509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:19.761040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:22.518130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:25.085129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:28.089883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:31.241031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:33.821454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:36.972738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:40.062807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:42.617836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:45.295754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:47.822128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:51.001256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:09.279139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:12.188017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:14.875224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:17.312028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:19.941522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:22.697840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:25.255935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:28.273990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:31.430453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:34.063829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:37.182925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:40.227359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:42.797756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:45.462540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:47.997449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:51.180804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:09.522872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:12.344304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:15.026252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:17.459132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:20.094846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:22.861103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:25.426510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:28.445064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:31.587729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:34.310896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:37.402106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:40.385647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:42.960706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:45.619440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:48.157730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:51.385642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:09.709698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:12.516909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:15.161974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:17.593870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:20.247993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:23.009623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:25.589944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:28.596761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:31.740489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:34.519486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:37.574066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:40.523511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:43.116582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:45.755711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:48.298060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:51.569701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:09.869246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:12.659741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:15.324971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:17.734396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:20.394729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:23.161306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:25.756836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:28.847396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:31.894964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:34.688363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:37.845100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:40.659978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:43.293978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:45.917027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:48.453798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:51.748631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:10.077165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:12.954005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:15.476410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:17.912950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:20.554050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:23.325975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:25.950638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:29.121118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:32.044548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:34.875388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:38.093990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:40.815919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:43.471551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:46.070658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:48.889082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:51.917563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:10.266310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:13.108567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:15.626322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:18.067592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:20.758987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:23.483954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:26.162333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:29.413193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:32.211972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:35.070658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:38.278955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:40.983141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:43.633490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:46.236156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:49.040367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:52.112952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:10.451030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:13.282894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:15.785794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:18.218050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:20.939973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:23.650028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:26.548022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:29.673536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:32.377972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:35.249407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:38.481089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:41.155799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:43.815360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:46.397344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:49.227708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:52.279055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:10.652330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:13.438570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:15.933806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:18.387624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:21.117516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:23.819587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:26.766898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:29.845246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:32.537754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:35.416183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:38.666527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:41.325538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:43.977193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:46.551965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:49.397700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:52.425582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:10.819883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:13.576322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:16.074601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:18.526252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:21.271056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:23.970419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:26.944237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:30.036445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:32.671170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:35.563811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:38.843421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:41.484734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:44.143507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:46.702037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:49.541092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:52.581816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:10.993384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:13.734813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:16.216567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:18.694321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:21.467474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:24.112804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:27.101297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:30.208643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:32.819360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:35.720690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:39.015597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:41.629121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:44.293214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:46.854860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:49.700859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:52.743597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:11.160820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:13.903731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:16.360194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:18.988412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:21.668563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:24.275524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:27.262801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:30.366649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:32.994620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:35.900173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:39.191281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:41.805943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:44.469272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:47.019093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:49.886476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:52.896458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:11.317682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:14.069011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:16.506197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:19.128533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:21.849204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:24.420163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:27.426569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:30.535268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:33.132235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:36.063727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:39.366785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:41.968625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:44.624135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:47.160268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:50.054701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:53.059156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:11.489654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:14.224642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:16.680426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:19.294539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:22.016030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:24.581665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:27.589470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:30.701615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:33.288960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:36.227295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:39.530021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:42.136859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:44.787835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:47.343373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:50.246440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:53.223523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:11.669700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:14.389668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:16.838846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:19.449402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:22.178388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:24.749968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:27.748988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:30.869479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:33.467006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:36.400754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:39.699657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:42.296361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:44.954430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:47.501325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:50.416492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:53.406270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:11.856454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:14.558525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:16.999708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:19.607268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:22.342490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:24.916297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:27.911269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:31.046732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:33.628465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:36.571473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:39.892239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:42.456643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:45.111699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:47.658352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-10T16:49:50.616277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-04-10T16:50:06.840090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
actor_1_fb_likesactor_2_fb_likesactor_3_fb_likesaspect_ratiobudgetcast_total_fb_likescontent_ratingcountrydirector_fb_likesdurationfacenumber_in_postergrossimdb_scoremovie_fb_likesnum_critic_for_reviewsnum_user_for_reviewsnum_voted_userstitle_year
actor_1_fb_likes1.0000.7550.6430.1450.3890.9550.0000.0000.1430.2120.1160.3170.0590.1120.3530.3630.4320.119
actor_2_fb_likes0.7551.0000.8540.1230.3830.8320.0000.0340.1180.1770.1060.347-0.0270.1000.2970.3260.3800.108
actor_3_fb_likes0.6430.8541.0000.0920.3470.7590.0000.0320.0980.1430.1150.339-0.0680.0870.2490.2970.3370.083
aspect_ratio0.1450.1230.0921.0000.2660.1440.0570.0000.0570.2160.0350.094-0.0310.0760.1930.1010.1230.290
budget0.3890.3830.3470.2661.0000.4090.0000.0460.1730.3360.0260.579-0.0570.0970.4190.4380.5010.143
cast_total_fb_likes0.9550.8320.7590.1440.4091.0000.0000.0160.1470.2140.1300.3530.0320.1180.3500.3690.4410.124
content_rating0.0000.0000.0000.0570.0000.0001.0000.129-0.0120.0170.022-0.2530.0490.0010.015-0.005-0.0420.036
country0.0000.0340.0320.0000.0460.0160.1291.0000.052-0.0430.0360.275-0.1280.0260.0430.1230.127-0.058
director_fb_likes0.1430.1180.0980.0570.1730.147-0.0120.0521.0000.1990.0080.1610.1430.0430.2260.2360.256-0.019
duration0.2120.1770.1430.2160.3360.2140.017-0.0430.1991.0000.0490.2440.3780.1070.2710.3630.358-0.075
facenumber_in_poster0.1160.1060.1150.0350.0260.1300.0220.0360.0080.0491.000-0.022-0.087-0.012-0.070-0.097-0.0410.064
gross0.3170.3470.3390.0940.5790.353-0.2530.2750.1610.244-0.0221.0000.0890.1040.4110.5360.6270.022
imdb_score0.059-0.027-0.068-0.031-0.0570.0320.049-0.1280.1430.378-0.0870.0891.0000.1420.3270.3510.424-0.153
movie_fb_likes0.1120.1000.0870.0760.0970.1180.0010.0260.0430.107-0.0120.1040.1421.0000.2900.1670.2150.273
num_critic_for_reviews0.3530.2970.2490.1930.4190.3500.0150.0430.2260.271-0.0700.4110.3270.2901.0000.7720.8080.357
num_user_for_reviews0.3630.3260.2970.1010.4380.369-0.0050.1230.2360.363-0.0970.5360.3510.1670.7721.0000.890-0.080
num_voted_users0.4320.3800.3370.1230.5010.441-0.0420.1270.2560.358-0.0410.6270.4240.2150.8080.8901.0000.029
title_year0.1190.1080.0830.2900.1430.1240.036-0.058-0.019-0.0750.0640.022-0.1530.2730.357-0.0800.0291.000

Missing values

2024-04-10T16:49:53.684830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-10T16:49:54.240061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

director_namenum_critic_for_reviewsdurationdirector_fb_likesactor_3_fb_likesactor_2_nameactor_1_fb_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_fb_likesactor_3_namefacenumber_in_posterplot_keywordsnum_user_for_reviewscountrycontent_ratingbudgettitle_yearactor_2_fb_likesimdb_scoreaspect_ratiomovie_fb_likes
0James Cameron723.0178.00.0855.0Joel David Moore1000.0760505847.0Action|Adventure|Fantasy|Sci-FiCCH PounderAvatar8862044834Wes Studi0.0avatar|future|marine|native|paraplegic3054.0USAPG-13237000000.02009.0936.07.91.7833000
1Gore Verbinski302.0169.0563.01000.0Orlando Bloom40000.0309404152.0Action|Adventure|FantasyJohnny DeppPirates of the Caribbean: At World's End47122048350Jack Davenport0.0goddess|marriage ceremony|marriage proposal|pirate|singapore1238.0USAPG-13300000000.02007.05000.07.12.350
2Sam Mendes602.0148.00.0161.0Rory Kinnear11000.0200074175.0Action|Adventure|ThrillerChristoph WaltzSpectre27586811700Stephanie Sigman1.0bomb|espionage|sequel|spy|terrorist994.0UKPG-13245000000.02015.0393.06.82.3585000
3Christopher Nolan813.0164.022000.023000.0Christian Bale27000.0448130642.0Action|ThrillerTom HardyThe Dark Knight Rises1144337106759Joseph Gordon-Levitt0.0deception|imprisonment|lawlessness|police officer|terrorist plot2701.0USAPG-13250000000.02012.023000.08.52.35164000
5Andrew Stanton462.0132.0475.0530.0Samantha Morton640.073058679.0Action|Adventure|Sci-FiDaryl SabaraJohn Carter2122041873Polly Walker1.0alien|american civil war|male nipple|mars|princess738.0USAPG-13263700000.02012.0632.06.62.3524000
6Sam Raimi392.0156.00.04000.0James Franco24000.0336530303.0Action|Adventure|RomanceJ.K. SimmonsSpider-Man 338305646055Kirsten Dunst0.0sandman|spider man|symbiote|venom|villain1902.0USAPG-13258000000.02007.011000.06.22.350
7Nathan Greno324.0100.015.0284.0Donna Murphy799.0200807262.0Adventure|Animation|Comedy|Family|Fantasy|Musical|RomanceBrad GarrettTangled2948102036M.C. Gainey1.017th century|based on fairy tale|disney|flower|tower387.0USAPG260000000.02010.0553.07.81.8529000
8Joss Whedon635.0141.00.019000.0Robert Downey Jr.26000.0458991599.0Action|Adventure|Sci-FiChris HemsworthAvengers: Age of Ultron46266992000Scarlett Johansson4.0artificial intelligence|based on comic book|captain america|marvel cinematic universe|superhero1117.0USAPG-13250000000.02015.021000.07.52.35118000
9David Yates375.0153.0282.010000.0Daniel Radcliffe25000.0301956980.0Adventure|Family|Fantasy|MysteryAlan RickmanHarry Potter and the Half-Blood Prince32179558753Rupert Grint3.0blood|book|love|potion|professor973.0UKPG250000000.02009.011000.07.52.3510000
10Zack Snyder673.0183.00.02000.0Lauren Cohan15000.0330249062.0Action|Adventure|Sci-FiHenry CavillBatman v Superman: Dawn of Justice37163924450Alan D. Purwin0.0based on comic book|batman|sequel to a reboot|superhero|superman3018.0USAPG-13250000000.02016.04000.06.92.35197000
director_namenum_critic_for_reviewsdurationdirector_fb_likesactor_3_fb_likesactor_2_nameactor_1_fb_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_fb_likesactor_3_namefacenumber_in_posterplot_keywordsnum_user_for_reviewscountrycontent_ratingbudgettitle_yearactor_2_fb_likesimdb_scoreaspect_ratiomovie_fb_likes
5026Olivier Assayas81.0110.0107.045.0Béatrice Dalle576.0136007.0Drama|Music|RomanceMaggie CheungClean3924776Don McKellar1.0jail|junkie|money|motel|singer39.0OtherR4500.02004.0133.06.92.35171
5027Jafar Panahi64.090.0397.00.0Nargess Mamizadeh5.0673780.0DramaFereshteh Sadre OrafaiyThe Circle45555Mojgan Faramarzi0.0abortion|bus|hospital|prison|prostitution26.0OtherNot Rated10000.02000.00.07.51.85697
5029Kiyoshi Kurosawa78.0111.062.06.0Anna Nakagawa89.094596.0Crime|Horror|Mystery|ThrillerKôji YakushoThe Cure6318115Denden0.0breasts|interrogation|investigation|murder|watching television50.0OtherR1000000.01997.013.07.41.85817
5032Ash Baron-Cohen10.098.03.0152.0Stanley B. Herman789.024848292.0Crime|DramaPeter GreeneBang4381186James Noble1.0corruption|homeless|homeless man|motorcycle|urban legend14.0USAR20000000.01995.0194.06.42.3520
5033Shane Carruth143.077.0291.08.0David Sullivan291.0424760.0Drama|Sci-Fi|ThrillerShane CarruthPrimer72639368Casey Gooden0.0changing the future|independent film|invention|nonlinear timeline|time travel371.0USAPG-137000.02004.045.07.01.8519000
5034Neill Dela Llana35.080.00.00.0Edgar Tancangco0.070071.0ThrillerIan GamazonCavite5890Quynn Ton0.0jihad|mindanao|philippines|security guard|squatter35.0OtherNot Rated7000.02005.00.06.32.3574
5035Robert Rodriguez56.081.00.06.0Peter Marquardt121.02040920.0Action|Crime|Drama|Romance|ThrillerCarlos GallardoEl Mariachi52055147Consuelo Gómez0.0assassin|death|guitar|gun|mariachi130.0USAR7000.01992.020.06.91.370
5037Edward Burns14.095.00.0133.0Caitlin FitzGerald296.04584.0Comedy|DramaKerry BishéNewlyweds1338690Daniella Pineda1.0written and directed by cast member14.0USANot Rated9000.02011.0205.06.42.35413
5038Scott Smith1.087.02.0318.0Daphne Zuniga637.024848292.0Comedy|DramaEric MabiusSigned Sealed Delivered6292283Crystal Lowe2.0fraud|postal worker|prison|theft|trial6.0OtherR20000000.02013.0470.07.72.3584
5042Jon Gunn43.090.016.016.0Brian Herzlinger86.085222.0DocumentaryJohn AugustMy Date with Drew4285163Jon Gunn0.0actress name in title|crush|date|four word title|video camera84.0USAPG1100.02004.023.06.61.85456